Data Science in the Cloud Overcoming Challenges and Maximizing Opportunities for Machine Learning
Keywords:
data science; knowledge intensive process mapping; web scraping; business intelligence in public management; process automation., Crowdsourcing, Trust Score, Fact checking, fingerprint template protection, minutiae points, cancelable and irrevocable templates, authentication.Abstract
As Data Science is a rapidly evolving field in which large amounts of data are analyzed to derive meaningful insights using machine learning-based techniques. In contrast, cloud computing offers a scalable and cost-effective platform for storing and processing data for machine learning-based applications. The combination of data science and cloud computing has emerged as a powerful tool for organizations seeking a competitive advantage through data- driven decision-making. The problem statement is that traditional machine learning approaches frequently involved storing data on local servers and analyzing it with specialized tools. However, this approach can be costly, time-consuming, and unscalable in the face of large data volumes. Many organizations are turning to cloud-based machine-learning platforms to address these challenges.
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